Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems
Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems
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DOI：
10.1016/j.ijpe.2012.10.016
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发表时间：
2013-09-01
影响因子：
12
通讯作者：
Scholz-Reiter, Bernd
中科院分区：
文献类型：
--
作者：
Pickardt, Christoph W.;Hildebrandt, Torsten;Scholz-Reiter, Bernd
我们提出了一种两阶段的超启发式算法，用于生成一组特定于工作中心的调度规则。该方法结合了遗传规划（GP）算法和进化算法（EA），前者从基本作业属性演化出复合规则，后者搜索工作中心规则的最佳分配。在半导体制造的一个复杂的动态作业车间问题上，对超启发式算法的两个组成部分和规则进行了测试。结果表明，所有三种超启发式都能够生成（一组）规则，这些规则的平均加权延迟率明显低于任何基准规则。此外，两阶段方法被证明优于GP和EA超启发式方法，因为它在两个不同的启发式搜索空间上进行优化，这两个空间似乎挖掘了不同的优化潜力。生成的规则集对于操作条件中的大多数更改也是健壮的。(C) 2012 Elsevier B.V.版权所有
We propose a two-stage hyper-heuristic for the generation of a set of work centre-specific dispatching rules. The approach combines a genetic programming (GP) algorithm that evolves a composite rule from basic job attributes with an evolutionary algorithm (EA) that searches for a good assignment of rules to work centres. The hyper-heuristic is tested against its two components and rules from the literature on a complex dynamic job shop problem from semiconductor manufacturing. Results show that all three hyper-heuristics are able to generate (sets of) rules that achieve a significantly lower mean weighted tardiness than any of the benckmark rules. Moreover, the two-stage approach proves to outperform the GP and EA hyper-heuristic as it optimises on two different heuristic search spaces that appear to tap different optimisation potentials. The resulting rule sets are also robust to most changes in the operating conditions. (C) 2012 Elsevier B.V. All rights reserved.